Systems and methods for analyzing consumer sentiment with social perspective insight

ABSTRACT

The present invention relates to systems and methods for cloud based consumer sentiment analysis with social insights. Data is integrated from a plurality of data sources, including a structured data source, an unstructured data source, a social data source, and a syndicated data source. Key attributes are selected from the integrated data, and may be name value pair requests. From these key attributes, consumer segments, sentiments and attribute correlations may be generated. The segments are generated from the social data. The correlation is generated using clustering algorithms. In some embodiments, generating sentiment and generating correlations dynamically utilizes models according to attributes of the integrated data. Polarity, emotion and topicality may be calculated for the generation of visualizations.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 USC 119(e) to acommonly-owned provisional application entitled “Saama Fluid AnalyticsEngine (SFAE)”, Application No. 62/124,799, filed on Jan. 5, 2015, andalso to a commonly-owned provisional application entitled “Cloud BasedConsumer Sentiment with Social Perspective Insight (CSSPI)”, ApplicationNo. ______, filed on ______, both of which are incorporated herein byreference for all purposes.

The present application also is a continuation-in-part and claimspriority to a commonly-owned application entitled “AbstractlyImplemented Data Analysis Systems and Methods Therefor”, applicationSer. No. 14/971,885, filed on Dec. 16, 2015, which is incorporatedherein by reference for all purposes.

Additionally, the present application is a continuation-in-part andclaims priority to a commonly-owned application entitled “Data AnalysisUsing Natural Language Processing to Obtain Insights Relevant to anOrganization”, application Ser. No. 14/975,778, filed on Dec. 19, 2015,which is incorporated herein by reference for all purposes.

BACKGROUND

The present invention relates to systems and methods for generating,analyzing and visualizing consumer sentiments with social insights andby consumer segments. Such systems and methods enable businesses to moreefficiently drive business strategies that are responsive to consumeremotions and sentiments, in order to increase one or more businessobjectives.

Businesses are increasingly competitive as information access becomesmore widespread. In order to remain viable, businesses must rely uponadvancements in marketing, price optimization, and understanding aconsumer's reaction to a product or business activity. Typically, datapoor mechanisms were employed in order to collect information onconsumers' sentiments. These methodologies included surveys, focusgroups, and other targeted research. However, there is a major drawbackwith these techniques due to the generally low sample size, lack ofcandidness on behalf of those being surveyed, and inaccurate reflectionof the consumer base in the surveyed group.

As social media and other online platforms have become more prominentfeatures in everyday lives, more and more businesses have dedicatedresources in order to understand and market using these new channelsinto potential customers. Moreover, in addition to being a usefulvehicle for delivering a message to consumers, these platforms provideunique insights into consumer sentiments.

In addition to social networks, we live in an age where vast amounts ofdata are being collected. These various data sources, in conjunctionwith newly available social network data, allows for unprecedentedabilities to analyze consumer sentiment. However, analyzing this vastdata pool is fraught with logistic and technical difficulties. Toooften, superfluous information obscures the insights that can be gainedfrom the data. Misdirection and false conclusions are common; thus,businesses are cautious to rely too heavily upon such “big data”analytics.

There are existing software applications for performing dataanalytics-based business intelligence currently. These applicationspermit the acquisition of data, the organization of stored data, theapplication of business rules to perform the analytics, and thepresentation of the analytics result. In the past, such applicationsrequire the use of an expert system integrator company or highly skilledpersonnel in the IT department (often a luxury that only the largestcompanies can afford) since these tools require custom coding, customconfiguration and heavy customization.

Furthermore, new technologies are now available for data storage, dataacquisition, data analysis, and presentation. Big data or cloudcomputing (whether open-source or proprietary) are some examples of suchtechnologies. Some of these technologies have not yet been widelyadopted by the business intelligence industry. Being new, the level ofexpertise required to make use of these technologies is fairly highsince there are fewer people familiar with these technologies. Thistrend drives up the cost of implementing new business intelligencesystems or updating existing business intelligence systems, particularlyif the business desire to make use of the new technologies.

It is therefore apparent that an urgent need exists for systems andmethods generating, analyzing and visualizing consumer sentiments withsocial insights and by consumer segments. Such systems and methodsenable businesses to more efficiently drive business strategies that areresponsive to consumer emotions and sentiments, in order to increase oneor more business objectives.

SUMMARY

To achieve the foregoing and in accordance with the present invention,systems and methods for cloud based consumer sentiment analysis withsocial insights are provided. Such systems allow businesses to moreaccurately ascertain the feelings and emotions of a relevant consumersegment in order to drive a business objective.

In some embodiments, the systems and methods integrate data from aplurality of data sources. The integration may employ cloud-basedcomputing techniques. The plurality of data sources includes astructured data source, an unstructured data source, a social datasource, and a syndicated data source.

Next, key attributes are selected from the integrated data. The keyattributes may be name value pair requests. From these key attributes,consumer segments, sentiments and attribute correlations may begenerated. The segments are generated from the social data. Thecorrelation is generated using clustering algorithms. In someembodiments, generating sentiment and generating correlationsdynamically utilizes models according to attributes of the integrateddata.

Polarity, emotion and topicality for a target and an audience may becalculated. The audience may be one of the generated segments, in someembodiments. The polarity, emotion and topicality may be utilized togenerate a visualization of the correlations.

Note that the various features of the present invention described abovemay be practiced alone or in combination. These and other features ofthe present invention will be described in more detail below in thedetailed description of the invention and in conjunction with thefollowing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the present invention may be more clearly ascertained,some embodiments will now be described, by way of example, withreference to the accompanying drawings, in which:

FIG. 1 shows, in accordance with an embodiment of the invention, atypical existing business intelligence (BI) analytics system;

FIG. 2 shows, in accordance with an embodiment of the invention, theconceptual organization of the improved abstractly-implemented dataanalysis system (AI-DAS);

FIG. 3 shows, in accordance with an embodiment of the present invention,the details of one implementation of the abstractly-implemented dataanalysis system (AI-DAS) system;

FIG. 4 shows a system architecture of an example AI-DAS implementation;

FIG. 5 shows, in accordance with an embodiment of the invention, anexample workflow employing the runtime engine in order to performbusiness intelligence analysis;

FIG. 6 shows some of the technologies involved in implementing the datasourcing, data acquisition, data management, data analysis, datastaging, and data extraction;

FIG. 7 shows some of the technologies employed in implementing each ofthe technology stacks in the AI-DAS system;

FIG. 8 illustrates a block diagram of an example environment where acloud based consumer sentiment system operate, in accordance with someembodiments;

FIG. 9 illustrates a block diagram of an more detailed example of theconsumer sentiment system, in accordance with some embodiments;

FIG. 10 illustrates a logical diagram of the connectivity of the varioussubcomponents of the consumer sentiment system, in accordance with someembodiments;

FIG. 11 illustrates a logical diagram of the integration elements of thebare analytics framework, in accordance with some embodiments;

FIG. 12 illustrates a logical diagram of the integration elements of thesentiments, correlation and perspectives framework, in accordance withsome embodiments;

FIG. 13 is a flow diagram illustrating an example process for thegeneration and analysis of cloud based consumer sentiment with socialinsights, in accordance with some embodiments; and

FIGS. 14A and 14B are example computer systems capable of implementingthe system for consumer sentiment analysis, in accordance with someembodiments.

DETAILED DESCRIPTION

The present invention will now be described in detail with reference toseveral embodiments thereof as illustrated in the accompanying drawings.In the following description, numerous specific details are set forth inorder to provide a thorough understanding of embodiments of the presentinvention. It will be apparent, however, to one skilled in the art, thatembodiments may be practiced without some or all of these specificdetails. In other instances, well known process steps and/or structureshave not been described in detail in order to not unnecessarily obscurethe present invention. The features and advantages of embodiments may bebetter understood with reference to the drawings and discussions thatfollow.

Aspects, features and advantages of exemplary embodiments of the presentinvention will become better understood with regard to the followingdescription in connection with the accompanying drawing(s). It should beapparent to those skilled in the art that the described embodiments ofthe present invention provided herein are illustrative only and notlimiting, having been presented by way of example only. All featuresdisclosed in this description may be replaced by alternative featuresserving the same or similar purpose, unless expressly stated otherwise.Therefore, numerous other embodiments of the modifications thereof arecontemplated as falling within the scope of the present invention asdefined herein and equivalents thereto. Hence, use of absolute and/orsequential terms, such as, for example, “will,” “will not,” “shall,”“shall not,” “must,” “must not,” “first,” “initially,” “next,”“subsequently,” “before,” “after,” “lastly,” and “finally,” are notmeant to limit the scope of the present invention as the embodimentsdisclosed herein are merely exemplary.

The presently disclosed systems and methods are directed toward businessintelligence when a vast wealth of data is available. As previouslynoted, this area of “big data” is relatively new, and subject tomisinterpretation or unwieldy analysis. The present systems and methodsovercome these hurdles by introducing social insights and customersegmentation in order to deliver more accurate and impactful customerinsight analysis.

Also note that the present disclosure applies to any situation wheregroup sentiment analysis is desired. It should be readily understoodthat while business insights are of particular importance to thepresently disclosed systems and methods, the embodiments herein may beemployed within basic research institutions, by political campaigns, forgovernmental messaging, and the like. Thus, while particular focus isplaces on utilizing the disclosed systems and methods for the generationof business insights, it should be readily understood that the scope ofthis disclosure extends to any purpose of sentiment analysis.

Lastly, note that the following description will be provided in a seriesof subsections for clarification purposes. These following subsectionsare not intended to artificially limit the scope of the disclosure, andas such any portion of one section should be understood to apply, ifdesired, to another section.

I. Abstractly-Implemented Data Analysis System

Embodiments of the invention relate to methods and apparatuses forcreating data analysis systems for generating insights (also known asresults) from a plurality of data sources without requiring thedesigner/implementer of the data analysis system or the user tounderstand complicated technology details such as for example coding,big data technology or high-end business analytics algorithms. These andother advantages of embodiments of the present invention will be betterunderstood with reference to the figures and discussions that follow.

In one or more embodiments, there exists an abstraction layer, known asa metadata layer, which contains metadata construct pertaining to datadefinition and design definition for subcomponents of the DA system. Thetechnology details (e.g., what technology is employed to implement aparticular subcomponent or to facilitate communication/storage) isabstracted and hidden from the designer/implementer and/or the user. Thesubcomponents communicate via APIs (application programming interface)to facilitate plug-and-play extensibility and replacement.

The metadata construct (which may be a file or library or collection offiles/libraries) contains information on the data definition (what datato expect; the format of the data; etc.) as well as the designdefinition (what to do with the receive data; how to store, organize,analyze, and/or output the data, etc.) as well as the data flow amongthe subcomponents. Preferably, the metadata construct is created inadvance during design time. At execution time, the execution enginereceives the BI query from a user, reads the data in a metadataconstruct that corresponds to that BI query and executes the BI queryusing data in the metadata construct to enable the subcomponents toretrieve and analyze data as well as output the BI insight.

In one or more embodiments, a user interface, which may be graphical, isemployed to create the metadata construct. In one or more embodiments,the metadata construct is an XML, file. The metadata constructrepresents a standardized manner to communicate with subcomponents ofthe BI system and contains instructions on how those subcomponents areto act, alone and in coordination with one another, to transform thedata from the various data sources into an analysis result such as abusiness insight. Since the metadata construct is an abstraction of theunderlying technology, embodiments of the invention allow implementersto create an end-to-end BI application that takes in a BI query andautomatically provide the BI insight simply by populating or creatingcontent in the appropriate metadata construct (as well as some lightcustomization for data output format if desired).

In this manner, an end-to-end DA application (such as a businessintelligence application) can be implemented without requiring the timeconsuming hard coding and expensive/scarce knowledge regarding theunderlying technologies/algorithms. Furthermore, by allowingsubcomponents to be implemented in a plug-and-play manner via APIs, itis possible to re-use or leverage existing analytics tools or partsthereof (such as an existing analysis module) by simply providing anappropriate API for the module and generating the data definition anddesign definition for it in the metadata construct. This is a hugeadvantage to customers who may have already invested substantially inexisting data analysis infrastructure.

These and other advantages of embodiments of the present invention willbe better understood with reference to the figures and discussions thatfollow.

FIG. 1 shows, in accordance with an embodiment of the invention, atypical existing business intelligence (BI) analytics system. In thisapplication, a business intelligence system is used as an example of adata analytics system but it should not be a limitation and thediscussion applies to data analytics systems in general.

A BI system 102 receives data from a variety of data sources 104 in avariety of formats. These data sources may include the corporatetransactional systems (such as sales or accounting or customerrelations), syndicated data (such as from 3rd party), web-based data(such as social media) and streaming data. The data may be stored in arelational database (RDBM) or in big data-related storage facilities(e.g., Hadoop, NoSQL). With regard to format, the data may be in anyformat including unstructured, structured, streaming, etc.

Data collection 106 pertains to activities required to acquire the datafrom the data sources 104. Data acquisition may employ ETL (Extract,Transform, Load) technologies or may employ custom connectors to theindividual data sources 102 for example. The data collection may happenin batches or in real time.

During data collection, business rules 108 may apply to pre-filterand/or pre-process the data. For example, some syndicated data may be ina specific format to suit the needs of a particular system unrelated toBI (such as reimbursement data from the reimbursement database ofinsurance companies, which data may include short-hand alphanumericcoding for common procedures and/or medication) and these formats mayneed to be converted for more efficient utilization by the analysiscomponent later.

The data collected is then stored in an aggregated data source 110 forready use by the analysis module. The aggregated data may be stored in arelational database (RDBM) or in big data-related storage facilities(e.g., Hadoop, NoSQL), with its formatting pre-processed to some degree(if desired) to conform to the data format requirement of the analysiscomponent.

The analysis component analyzes (120) the data using business rules 122and stores the BI insight in analyzed data store 124. The analysis mayemploy some custom analytics packages or may employ big data analysistechniques for example. At some point in time, the user may desire toknow the BI insight and thus information retrieval (130) is performed toobtain the BI insight from the analyzed data store 124 and to presentthe BI insight to business applications 132. These presentation methodsmay be self-service or interactive (such as through a webpage thatallows the user to sort and organize the data in various ways). Thepresentation medium may be a thick client, a web or mobile applicationrunning on a desktop, laptop, or mobile device for example.

Underlying the above activities is a security and governance subsystem150 that handles house-keeping and system-related tasks such asscheduling jobs, data access authorization, user access authorization,auditing, logging, etc.

In the past, the implementation of BI system 102 typically involveshard-coding the components, creating custom code to enable thecomponents of FIG. 1 to interoperate and produce the desired BI insight.The system integration effort and custom development (160) require asubstantial investment of time and effort during the development,integration, and deployment stages. Because of the rapidly changingtechnology landscape, a typical company often does not have sufficientIT expertise in-house to build, maintain and/or deploy a BI system ifthat company desires to utilize the latest technology. Instead, the workis contracted out to integrator firms with special expertise at greatcost in each of the development, maintenance, deployment, and upgradephases.

The hard coding approach makes it difficult and/or expensive to upgradewhen new BI insight needs arise and/or when improved technology isavailable for the tasks of data acquisition, data analysis, and/or datapresentation. It also makes it difficult to re-use legacy subcomponentsthat the business may have already invested in in the past. This ismainly because of both the cost/time delay involved in re-coding a BIsystem and the predictable scarcity of knowledgeable experts when newtechnologies first arrive.

FIG. 2 shows, in accordance with an embodiment of the invention, theconceptual organization of the improved abstractly-implemented dataanalysis system (AI-DAS). The conceptual tasks that need to be performedin box 202 are analogous to those discussed in connection with FIG. 1.However, embodiments of the invention pre-integrate (220) thesubcomponents (to be discussed later in FIG. 3 and later figures) withplug-and-play capability in order to facilitate their upgradability andextensibility.

More importantly, there exists an abstraction layer, known as a metadatalayer 204. The metadata may be implemented by a file or library or acollection of files or libraries and contains data pertaining to thedata flow among the subcomponents of components implementing the threetasks of BI system 200 (data collection 206, data analysis 208, andanalysis result retrieval/presentation 210). The metadata may alsoinclude information about data definition and design definition for eachof the subcomponents. Generally speaking, data definition pertains tothe location where the data comes from and where it is to be outputted,the format of the data, and the like. Design definition generallypertains to the operation in each subcomponent including for examplewhat to do with the inputted data, how to store, organize, analyze,output the data, etc.

The metadata 204 is designed during design time in order to define theoperation of the subcomponents and the data flow among thesubcomponents, and by extension, the operation of the resulting BIsystem for a particular type of query. During design time, thedesigner/implementer is shielded or isolated from the technology detailsof the subcomponents. The designer/implementer task becomes one ofpopulating the metadata construct with sufficient information to alloweach subcomponent to know what data to expect and to output, and howeach subcomponent is to behave during execution. In an embodiment, agraphical user interface is provided to assist in the task of fillingout the data fields of the metadata. Because the implementer/designer ofthe BI system only needs to work at the high level of abstraction of themetadata layer, expensive skilled knowledge regarding the newesttechnology is not required. Further, because the system can be easilyreconfigured (simply by creating another metadata) to handle differentanalysis tasks or accommodate different/substitute subcomponents, re-useof many of the subcomponents is promoted.

At execution time, the business intelligence query from the user isintercepted and a metadata construct (file, library, or set offiles/libraries) appropriate to that business intelligence query isretrieved. The execution engine then reads and interprets the data inthe metadata in order to know how to utilize the subcomponents toperform tasks to arrive at the business intelligence insight requestedby the business intelligence query. Again, the user does not need knowthe details of the underlying technologies or even thepresence/structure of the metadata itself. As long as the user can inputa business intelligence query that can be parsed and understood by thebusiness intelligence system, the business intelligence system willautomatically select the appropriate metadata construct and willautomatically carry out the required tasks using data from the metadataconstruct and the subcomponents of the business intelligence system.

FIG. 3 shows, in accordance with an embodiment of the present invention,the details of one implementation of the abstractly-implemented dataanalysis system (AI-DAS) system 300. AI-DAS 300 includes three maincomponents: Data acquisition, data analysis, and data presentation.

Data acquisition 302 relates to getting the data, organizing the data,extracting the data, storing the data. As shown in box 310, the variousdata sources include unstructured data (e.g., freeform data such as thetext entered by patient comments or doctor/nurse comments), structureddata such as data enter into fields of a form, syndicated data such asdata purchased or received from third parties, transactional system datasuch as data directly obtained from the ERP system or the enterprisedata store of the company, social media data such as data from Facebook,Twitter, Instagram, and the like. The data may be received in batches ormay be streaming data. These are only examples of data sources that maybe employed for analysis by the AI-DAS 300.

Within data acquisition component 302, there exist a plurality ofsubcomponents shown as data acquisition-related subcomponents 320-330.Subcomponent 320 pertains to the task of data acquisition, which relatesto how the data is acquired from various sources 310. Subcomponent 322relates to data extraction, which contains the logic to extract the datasources 310. Subcomponent 324 pertains to data organization, whichcontains the logic to organize the extracted data. Subcomponent 326pertains to certain pre-processing of the data. For example, theextracted data is discovered (such as using parsing or artificialintelligence) processed (such as mapping) and aggregated. Splitting andmerging of various data items may also be done.

Subcomponent 328 pertain to additional higher level processing of thedata, if desired. Subcomponent 330 pertains to grouping data sourcesinto a transactional unit that can be processed as a single entity. Forexample, the total number of data sources may comprise hundreds of datasources available. However, for a particular business intelligencequery, only certain data resources are used. These can be groupedtogether in a single analytical entity for ease of administration.

Data analysis component 304 relates to analyzing the data and extractingmeaning from the aggregated data that is output by data acquisitioncomponent 302. Within data analysis component 304, there exists aplurality of subcomponents shown as data analysis-related subcomponents350-360. Subcomponent 360 relates to data exploration since at thisstage, it may not be known what the data contains. Artificialintelligence or pattern matching or keywords may be employed to look formeaning in the data. The data can be prepared and preprocessed in 358 toconvert the data into a format for use by the algorithm.

The three subcomponents 352, 354, and 356 represent the machine learningapproach that is employed for this example of FIG. 3. In subcomponent356, the model is selected which may be prebuilt or an external modelmay be integrated. In subcomponent 354, the model is trained and oncethe model is selected 356 and trained in 354, the model may be persisted352 to process the incoming data. Post-processing 350 relates topreparing the data for presentation, which occurs in data presentationcomponent 306.

Data presentation subcomponent 306 relates to how to present the data tothe user. The data may be presented using traditional and advancedvisualization methods (378) such as infographics, maps, and advancedcharts. Legacy presentation tools may also be employed via standard orcustomized extensions and plug-ins 376. Tool may be provided for theuser to filter and to drill down the data, essentially allowing the userto explore the result in 374. The data may also be exported into adesired data format for later use. This is shown 372 wherein the examplethe formats are PowerPoint, PDF, Excel, PNG. The presentation mechanismcan be interactive or static, and presentation data can be sent via thecloud and/or internal network to a laptop, desktop, or mobile device(370).

Subcomponent 380 relates to data governance, system governance, jobtracking and management, and error handling. These are tasks related tomanaging the hardware and software resources to perform the analysis.Subcomponent 382 relates to control of data access and job execution anduser access. Thus, there is shown authentication, authorization,notification, scheduling of jobs. Logging, auditing, and intelligentcaching of data to improve execution speed are also shown in 382.

A metadata construct 392 is shown interposing between the user 394 andthe components/subcomponents of the AI-DAS 300. As mentioned, thismetadata contains the higher level abstraction of the subcomponents andallow the AI-DAS to be implemented without knowing the complexunderlying technology details.

All the subcomponents shown in each of the data acquisition, dataanalysis, and data presentation components can be either off-the-shelf,custom created, open-source, or legacy subcomponents. For plug-and-playimplementation, these subcomponents preferably communicate using the APImodel. These subcomponents can be implemented on an internal network, inthe cloud using a cloud-based computing paradigm (such as through AmazonWeb Services or Google Web), or a mixture thereof. Generically speaking,these are referred to herein as computer resource.

FIG. 4 shows a system architecture of an example AI-DAS implementation,including user interface devices 402A and 402B (desktop/laptop andmobile devices respectively). These devices can access the AI-DAS system400 via in the Internet 404 using for example the HTTPS protocol.Firewall/security group 406 and cloud 408 show thatcomponents/subcomponents and data storage employed to implement theAI-DAS may reside in the cloud or may reside behind the firewall withina company or can be both.

The AI-DAS operation is governed by a load balancer 410 which loadbalances multiple copies of the AI-DAS runtime engine 420. For ease ofillustration, the multiple implementations of the AI-DAS runtime engine420 are shown at both the top and the bottom of FIG. 4. At the top ofFIG. 4, these multiple instantiations of the AI-DAS runtime engineinteracts with the API (such as Secure RESTful API) that governs thecommunication between subcomponents in the data acquisition component,the data analysis component, and the data presentation component. TheAI-DAS runtime engine also reads the metadata (implemented as an XML inthe example) and interpret the XML then delegates the tasks specified bythe XML to various subcomponents in the data acquisition, data analysis,and data presentation subcomponents.

Data is received from external data sources 422 and is processed viadata acquisition subcomponent 430, data analysis subcomponent 432, anddata presentation subcomponent 434. The data is processed by dataacquisition subcomponent 422 via ingestion module and transformation,auditing, analytical entities. The aggregated and analyzed data is thenstored in the appropriate data store (such as Hadoop big data store),relational database RDBMS, or noSQL data store.

The data analysis subcomponent 432 represents the intelligence componentand includes therein statistical models and modules related to modeltraining, pre- and post-processing. The data presentation subcomponent434 includes the various responsive (interactive) user interfaces andmay include traditional presentation tools such as charts, maps, events,filters. As shown in FIG. 4, there may be multiple instantiations ofeach of the components/subcomponents in addition to differentinstantiations of multiple runtime engines, all of which can be loadbalanced to horizontally scale the analytics system to accommodate anyvolume of analytics jobs.

Generally speaking, there are the two separate phases of building anddelivering an AI-DAS end-to-end application. One of the requirements isthat the subcomponents employ APIs, allowing them to interoperate usingan API model and to receive instructions from the execution engine asthe execution engine interprets the metadata. Thus, during design time,a designer/implementer may create the metadata XML that includes thedata definition and design definition for the subcomponents. Once thedesign phase is completed, the system is deployed and ready to produceanalysis result during execution time.

During execution time (which occurs when a user inputs a query), themetadata XML is selected for the query, interpreted and executed by theAI-DAS engine, which metadata specifies how each subcomponent wouldbehave based on the parameters specified in the metadata. The metadataalso specifies the format of the data that the subcomponents exchangeamong each another, as well as the overall data flow from data intakefrom multiple data sources to the presentation of the analytic resultsto the user interface devices.

FIG. 5 shows, in accordance with an embodiment of the invention, anexample workflow employing the runtime engine in order to performbusiness intelligence analysis. During design time,administrative/developer 502 employs UI device (which may be for examplea laptop or desktop computer) 504 to configure the metadata (such as theXML). This is shown as step 1 of FIG. 5. Preferably, a graphical userinterface is employed to simplify the task of populating the metadatafields. At this time, any custom HTML templates and custom javascriptcan also be created to format the output if desired.

With respect to the metadata XML, the admin/developer 502 may define thedata. That is the admin/developer may specify where the data comes fromand the type of data that is inputted (e.g., free-form, stream,structured, and the like). The admin/developer 502 may also specify thedesign definition, that is how the data is used in the application. Thedesign definition defines the goal of the data analysis. For example,one goal may be to perform sentiment analysis on conversation data aboutnurses. Another goal may be to discover the top three hot topics in theunstructured data that is received. Another goal may be to importcertain columns in a relational database and run it through a certainmodel to identify patients who are not satisfied.

The design definition can also specify the manner in which data isoutputted. Examples of such specification include the format and thedevices and/or data presentation technologies involved. These are shownin 510, 512, and 514 of FIG. 5.

Then during execution time the user may use a UI device to issue a HTTPrequest (step 2) that is received by the HTML page 520. The HTML page520 parses the request then issues another request (step 3) to acquirethe appropriate associated metadata XML that contains the datadefinition and the design definition relevant to the instant query.

With this data definition and design definition in the XML, the AI-DASengine then makes a call to the server-side component for connecting toresources to obtain data and to analyze data. Note that these datasources and the manner in which the data is analyzed are specified bythe XML in the data definition and design definition 514 and 512. Thisis step 4.

In step 5, the data source connections are selected to connect to theappropriate data sources 530A, 530B, and 530C to obtain data foranalysis. The analysis is performed by the server subcomponent thatemploys, in the example of FIG. 5, the RESTful web service 540. Analysisincludes performing data service (design generation and dataintegration) as well as data access and analysis (542 and 544) inaccordance with the definition in the XML.

Once data analysis is completed by the AI-DAS server, the servercomponent returns the analyzed data (step 6) and the response (step 7)is provided to the HTML page. The response may be formatted inaccordance with the definition in the XML page. The response is thenreturned to the UI device 504 for viewing by the user and forinteraction by the user (step 8)

FIG. 6 shows some of the technologies involved in implementing the datasourcing, data acquisition, data management, data analysis, datastaging, and data extraction. Some of these technologies are well-knownin distributed computing/big data for storage (such as Hadoop) and foranalysis (such as MapReduce, Spark, Mahout). Workflow engine may beprovided by OOZIE while system administration may be provided by Ambariand Apache Falcon.

It should be noted that the technology details of FIG. 6 are hidden fromthe design/implementer during design time since the designer/implementerneeds only be concerned with the metadata population and any optionalHTML/JS customization for data outputting. These technology details arealso hidden from the customer/user during execution since thecustomer/user only needs to issue a query that can be parsed to obtainthe associated XML, and the rest of the analysis and underlying detailsregarding technology are handled transparently.

FIG. 7 shows some of the technologies employed in implementing each ofthe technology stacks in the AI-DAS system. For example, the data layer702 may be implemented by (704) transactional, enterprise data warehouse(EDW), syndicated, social, and unstructured data. However, any otheralternative data source (706) may be employed.

Connectors layer (712) may be implemented by (714) ETL, Java, Webservices. However, any appropriate alternative integration connectingtechnology (716) may also be employed. The same applies to the datamodel layer 722, data access layer 724, analysis layer 726, andvisualization layer 728. For each of these layers, there is acorresponding list of example technologies in the stack 750 as well asin alternatives/integration 752. One important point to note is sincethe underlying technology is hidden, the layers (such as data,connectors, data model, data access, and the analysis, visualization)may be implemented by any appropriate technology, including legacytechnology.

As can be appreciated from the foregoing, embodiments of the inventionrenders it unnecessary for the designer/implementer to know or tomanipulate complex technology in the implementation, maintenance, orupgrade of a data analysis system. The metadata layer abstracts thesecomplex technology details away and provide standardized,easy-to-implement way of specifying how the DAS system should operate tosolve any particular analysis problem.

As long as the subcomponents comply with the API model forinteroperability, the underlying technology may be interchangeable on aplug-and-play basis. The ease with which the AI-DAS system can beimplemented (due to the abstraction of the complex technology detailsaway from the designer/implementer) encourages exploration and rendersimplementation, maintenance, and upgrade of a data analysis systemsubstantially simpler, faster, and less costly than possible in thecurrent art.

II. Consumer Sentiment Analysis

Now that underlying technology for an abstractly-implemented dataanalysis system has been described in considerable detail, attentionshall now be shifted to the generation and analysis of consumersentiment. To facilitate the discussion, FIG. 8 provides an exampleschematic block diagram for an example environment 800 where consumersentiment may be determined and analyzed. In this example environment, aplurality of consumer 802 a-n are seen providing information, via anetwork 804, that is eventually included within more than one datasources 806 a-m. The network 804 may be any type of cellular, IP-basedor converged telecommunications network, including but not limited toGlobal System for Mobile Communications (GSM), Time Division MultipleAccess (TDMA), Code Division Multiple Access (CDMA), OrthogonalFrequency Division Multiple Access (OFDM), General Packet Radio Service(GPRS), Enhanced Data GSM Environment (EDGE), Advanced Mobile PhoneSystem (AMPS), Worldwide Interoperability for Microwave Access (WiMAX),Universal Mobile Telecommunications System (UMTS), Evolution-DataOptimized (EVDO), Long Term Evolution (LTE), Ultra Mobile Broadband(UMB), Voice over Internet Protocol (VoIP), Unlicensed Mobile Access(UMA), etc.

The network 804 can be any collection of distinct networks operatingwholly or partially in conjunction to provide connectivity between theoriginators of the data (consumer 802 a-n), the data sources 806 a-m,and a consumer sentiment analyzer system 810. In some embodiments,information and communications to and from the consumer 802 a-n, thedata sources 806 a-m, and the consumer sentiment analyzer system 810 canbe achieved by an open network, such as the Internet, or a privatenetwork, such as an intranet and/or the extranet. The consumer 802 a-n,the data sources 806 a-m, and the consumer sentiment analyzer system 810can be coupled to the network 804 (e.g., Internet) via a dial-upconnection, a digital subscriber loop (DSL, ADSL), cable modem, wirelessconnections, direct fiber connections and/or any other types ofconnection.

The data sources 806 a-m may include corporate transactional systems(such as sales or accounting or customer relations), syndicated data(such as from 3rd party), web-based data (such as social media) andstreaming data. The data may be stored in a relational database (RDBM)or in big data-related storage facilities (e.g., Hadoop, NoSQL). Withregard to format, the data may be in any format including unstructured,structured, streaming, etc. Data acquisition from the data sources 806a-m may employ ETL (Extract, Transform, Load) technologies or may employcustom connectors to the individual data sources 806 for example. Thedata collection may happen in batches or in real time. As previouslydiscussed, during data collection, business rules may be applied topre-filter and/or pre-process the data

Although not illustrated, in some embodiments there may be additionalintermediary objects or entities that are currently omitted for the sakeof clarity. For example, a consumer 802 may purchase a product from amerchant, which collects transaction data. The merchant may then provideaggregate transaction data to a retailer consortium, which mayultimately be used to generate a dataset of product sales trends. Suchdata may be very useful to the consumer sentiment analyzer system 810,and may be a data store 806 utilized for the sentiment analysis. Themerchant and consortium, in this example scenario are purposefullyomitted in order to not overly clutter the figure. However, it should benoted that this does not therefore limit the data stores 806 a-m toreceiving data directly from the consumers 802 a-n, nor does it requirethe network 804 as an intermediate.

The consumer sentiment analyzer system 810 is able to query the variousdata stores 806 a-m in order to generate consumer sentiment insights.These insights, in some embodiments, may be aggregated by variousconsumer segments, and may further take into account social mediainsights and impacts. In some embodiments, the consumer sentimentanalyzer system 810 includes many structural components, including loadbalancing servers, data analytics servers working in parallel to sort,integrate, extract, model and eventually visualize the data from thedata stores. Local databases within the consumer sentiment analyzersystem 810 are used to store transient data signals, and longer livedresults from the analysis of data stores. Local databases may likewisebe utilized to cache information from the data stores 806 a-m in orderto enhance performance, and ensure operability if the data store 806becomes disrupted.

FIG. 9 provides more details into one embodiment of the consumersentiment analyzer system 810 at issue in this disclosure. In thisexample system, different or partially different data sources 806 areutilized by two subcomponents of the consumer sentiment analyzer system810. The first subcomponent is a bare analytics framework 910, which iscoupled to a sentiments, correlation and perspective framework 930 viaan integration bridge 920. The bare analytics framework 910 may becapable to compiling the complete analysis to generate the output 940for storage and viewing by downstream users or business applications.

In this embodiment, the bare analytics framework 910 has the capabilityto house any structured and unstructured data for analytics on the cloudenvironment. The sentiments, correlation and perspective framework 930includes advanced algorithms to analyze data collected from social,syndicated and unstructured data. The analysis is based on the keyparameters that are requested by the user or business. In someembodiments, the parameters may be configured via the interfacedescribed above, or may be requested as part of the system build.

The integration bridge 920 joins the two subsystems. The integrationbridge 920 is capable of receiving a name value pair from the bareanalytics framework 910 and passes it to the sentiments, correlation andperspective framework 930 for modeling and analysis. In turn, theintegration bridge 920 is capable of passing the resulting correlatedprescriptive information back from the correlation and perspectiveframework 930 to the bare analytics framework 910. In some embodiments,the sentiments, correlation and perspective framework 930 may be an openarchitecture configured to switch models and algorithms used for theanalysis of incoming data based upon need.

Turning toward FIG. 10, a more detailed example of the logicalconnectivity of the various components of the consumer sentimentanalyzer system 810 is provided. Again, it can be seen that data sources806 provide information to both the correlation and perspectiveframework 930 and the bare analytics framework 910, respectively. Thedata sources 806 provide input to integration elements 1040 and 1010,respectively, which generates a data signal from the source data. Sourcedata may be structured or unstructured, so the integration elements 1040and 1010 transform the source data into standardized formats, andperform preconditioning of the data. Subsequently, attribution selectionelements 1050 and 1020, respectively, select out the data that isrelevant for sentiment analysis. This selected data may be provided tothe integration bridge 930 in the form of name value pair requests,which may then be provided to at least one of the social segmentationelement 1060, the sentiments mapping element 1070, and the correlationmapping element 1080. These elements may leverage advanced models inorder to perform their analysis.

The social segmentation element 1060 may be used to aggregate usersbased upon similar features into discrete segments based upon socialnetwork data. Examples of segment dimensions include demographics,familial status, education level, political views, age, similarinterests, affiliations, wealth and/or income levels, or the like. Thesentiments mapping element 1070 may be able to determine the sentiment aconsumer or group of consumers has, at any given time period, regardingany target. For a business, the target is most often a product, brand,advertisement campaign, or business practice. However, in someembodiments, the user of the system may be able to configure any target,and audience, for sentiment analysis. For example, in relation to apolitical campaign, a campaign manager may wish to examine the sentimentfor a candidate policy (target) among a social segment including membersof the candidate's party between the ages of 30-55, with a median incomeabove $50,000 (audience).

Lastly, the correlation mapping element 1080 may be utilized inconjunction with sentiment analysis, or as an independent analysisfeature, in order to correlate attributes of a given business orfunction. This correlation may utilize clustering algorithms,multi-objective optimizations, and/or distance functions to correlateattributes. The correlation mapping element 1080 may identifycorrelations across the various data sources, which are often verydiverse and independent from one another. Initially the systemcalculates polarity, emotions and topicality (collectively referred toas PET) for a given query. This PET value is used to identifycorrelations across structured, unstructured and syndicated data, insome particular embodiments.

Structurally, the social segmentation element 1060, the sentimentsmapping element 1070, and the correlation mapping element 1080 reside aspart of the correlation and perspective framework 930, in someembodiments. However, from a logical perspective, these subcomponentsmay be viewed as interacting directly with the integration bridge 920.Resulting analysis from each of the social segmentation element 1060,the sentiments mapping element 1070, and the correlation mapping element1080 is thus returned via the integration bridge 920, and may eventuallybe presented to a user via a visualization element 1030. Thevisualization element 1030 may reside as part of the bare analyticsframework 910.

Turning to FIG. 11, a more detailed view is provided of the integrationelement 1010 found within the bare analytics framework 910. The datasources leveraged by the integration element 1010 include structureddata 806 a and unstructured data 806 b. These data sources areintegrated by a structured data integration element 1110 and anunstructured data integration element 1120, respectively. The result ofsaid integration is a structured data signal 1115 and an unstructureddata signal 1125, respectively.

Similarly, FIG. 12 provides a more detailed view of the integrationelement 1040 found within the correlation and perspective framework 930.The data sources leveraged by the integration element 1040 includesocial data 806 c, syndicated data 806 d and unstructured data 806 e.These data sources are integrated by a social data integration element1210, a syndicated data integration element 1220, and an unstructureddata integration element 1230, respectively. The result of saidintegration is a social data signal 1215, a syndicated data signal 1225,and an unstructured data signal 1235, respectively.

Now, turning to FIG. 13, an example process 1300 is provided for thegeneration of a correlation visualization using diverse data sourceswithin such a business analytics system. As discussed, the data isinitially collected from the various data sources, at 1310. The dataincludes structured data, unstructured data, social data, and syndicateddata. The collected data may be parsed according to business rules, inorder to remove redundant, erroneous, or irrelevant data. This data maybe integrated into various data signals, at 1320, which are provided toan attribute selector. The data signals include structured data signals,unstructured data signals, social data signals, and syndicated datasignals.

The selector may identify key attributes within the data signals, andprovide these attributes as name value pairs to various analysiselements, at 1330. The first analysis utilizes the social data signal tosegment the data by a desired parameter, at 1340. In some embodiments,the next analysis step is to determine sentiment, utilizing mapping andmodeling techniques, for a target and a given audience, at 1350. In somecases the audience is a segment identified previously. In alternateembodiments, the sentiment analysis may be skipped for a reduction inprocessing requirements, or if the data is insufficient to generateaccurate sentiment data.

Additionally, the correlations between attributes across the diversedata signals may be correlated, at 1360, by a correlation mappingelement. Models may be leveraged to calculate the polarity, emotions andtopicality (PET) using the segments and maps, at 1370, in someembodiments. The correlation visualizations are then generated from theresulting PET, at 1380. These visualizations may be leveraged bybusinesses or other users in order to formulate business strategies, oras factors in business decision making.

III. System Embodiments

Now that the systems and methods for the sentiment analysis by consumersegment for the generation of correlation visualizations has beendescribed in considerable detail, attention shall now be focused uponsystems capable of executing the above functions. To facilitate thisdiscussion, FIGS. 14A and 14B illustrate a Computer System 1400, whichis suitable for implementing embodiments of the present invention. FIG.14A shows one possible physical form of the Computer System 1400. Ofcourse, the Computer System 1400 may have many physical forms rangingfrom a printed circuit board, an integrated circuit, and a smallhandheld device up to a huge super computer. Computer system 1400 mayinclude a Monitor 1402, a Display 1404, a Housing 1406, a Disk Drive1408, a Keyboard 1410, and a Mouse 1412. Disk 1414 is acomputer-readable medium used to transfer data to and from ComputerSystem 1400.

FIG. 14B is an example of a block diagram for Computer System 1400.Attached to System Bus 1420 are a wide variety of subsystems.Processor(s) 1422 (also referred to as central processing units, orCPUs) are coupled to storage devices, including Memory 1424. Memory 1424includes random access memory (RAM) and read-only memory (ROM). As iswell known in the art, ROM acts to transfer data and instructionsuni-directionally to the CPU and RAM is used typically to transfer dataand instructions in a bi-directional manner. Both of these types ofmemories may include any suitable of the computer-readable mediadescribed below. A Fixed Disk 1426 may also be coupled bi-directionallyto the Processor 1422; it provides additional data storage capacity andmay also include any of the computer-readable media described below.Fixed Disk 1426 may be used to store programs, data, and the like and istypically a secondary storage medium (such as a hard disk) that isslower than primary storage. It will be appreciated that the informationretained within Fixed Disk 1426 may, in appropriate cases, beincorporated in standard fashion as virtual memory in Memory 1424.Removable Disk 1414 may take the form of any of the computer-readablemedia described below.

Processor 1422 is also coupled to a variety of input/output devices,such as Display 1404, Keyboard 1410, Mouse 1412 and Speakers 1430. Ingeneral, an input/output device may be any of: video displays, trackballs, mice, keyboards, microphones, touch-sensitive displays,transducer card readers, magnetic or paper tape readers, tablets,styluses, voice or handwriting recognizers, biometrics readers, motionsensors, brain wave readers, or other computers. Processor 1422optionally may be coupled to another computer or telecommunicationsnetwork using Network Interface 1440. With such a Network Interface1440, it is contemplated that the Processor 1422 might receiveinformation from the network, or might output information to the networkin the course of performing the above-described big data analysis forconsumer sentiment. Furthermore, method embodiments of the presentinvention may execute solely upon Processor 1422 or may execute over anetwork such as the Internet in conjunction with a remote CPU thatshares a portion of the processing.

Software is typically stored in the non-volatile memory and/or the driveunit. Indeed, for large programs, it may not even be possible to storethe entire program in the memory. Nevertheless, it should be understoodthat for software to run, if necessary, it is moved to a computerreadable location appropriate for processing, and for illustrativepurposes, that location is referred to as the memory in this disclosure.Even when software is moved to the memory for execution, the processorwill typically make use of hardware registers to store values associatedwith the software, and local cache that, ideally, serves to speed upexecution. As used herein, a software program is assumed to be stored atany known or convenient location (from non-volatile storage to hardwareregisters) when the software program is referred to as “implemented in acomputer-readable medium.” A processor is considered to be “configuredto execute a program” when at least one value associated with theprogram is stored in a register readable by the processor.

In operation, the computer system 1400 can be controlled by operatingsystem software that includes a file management system, such as a diskoperating system. One example of operating system software withassociated file management system software is the family of operatingsystems known as Windows® from Microsoft Corporation of Redmond, Wash.,and their associated file management systems. Another example ofoperating system software with its associated file management systemsoftware is the Linux operating system and its associated filemanagement system. The file management system is typically stored in thenon-volatile memory and/or drive unit and causes the processor toexecute the various acts required by the operating system to input andoutput data and to store data in the memory, including storing files onthe non-volatile memory and/or drive unit.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is, here and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the methods of some embodiments. The requiredstructure for a variety of these systems will appear from thedescription below. In addition, the techniques are not described withreference to any particular programming language, and variousembodiments may, thus, be implemented using a variety of programminglanguages.

In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in a client-server network environment or as a peermachine in a peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a laptop computer, a set-top box (STB), apersonal digital assistant (PDA), a cellular telephone, an iPhone, aBlackberry, a processor, a telephone, a web appliance, a network router,switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine.

While the machine-readable medium or machine-readable storage medium isshown in an exemplary embodiment to be a single medium, the term“machine-readable medium” and “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” and “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing, encodingor carrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresently disclosed technique and innovation.

In general, the routines executed to implement the embodiments of thedisclosure may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and when read andexecuted by one or more processing units or processors in a computer,cause the computer to perform operations to execute elements involvingthe various aspects of the disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

In sum, the present invention provides systems and methods forgenerating, analyzing and visualizing consumer sentiments with socialinsights and by consumer segments. Such systems and methods enablebusinesses to more efficiently drive business strategies that areresponsive to consumer emotions and sentiments in order to increase abusiness objective.

While this invention has been described in terms of several embodiments,there are alterations, modifications, permutations, and substituteequivalents, which fall within the scope of this invention. Althoughsub-section titles have been provided to aid in the description of theinvention, these titles are merely illustrative and are not intended tolimit the scope of the present invention.

It should also be noted that there are many alternative ways ofimplementing the methods and apparatuses of the present invention. It istherefore intended that the following appended claims be interpreted asincluding all such alterations, modifications, permutations, andsubstitute equivalents as fall within the true spirit and scope of thepresent invention.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the disclosure can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further embodiments of thedisclosure.

What is claimed is:
 1. A computer-implemented method for generatingconsumer sentiment and attribute correlation visualizations from dataobtained from a plurality of data sources, comprising: integrating datafrom a plurality of data sources; selecting key attributes from theintegrated data; generating at least one consumer segment using theintegrated data; generating sentiment for the at least one consumersegment using the integrated data; generating correlations betweenattributes of the integrated data; and generating a visualization of thesegment, sentiment and correlations.
 2. The method of claim 1 whereinthe integration employs a cloud-based computing technique.
 3. The methodof claim 1 wherein the plurality of data sources includes a structureddata source, an unstructured data source, a social data source, and asyndicated data source.
 4. The method of claim 3 wherein the generatingthe at least one consumer segment employs the social data source.
 5. Themethod of claim 1 further comprising calculating polarity, emotion andtopicality for a target and an audience.
 6. The method of claim 5wherein the audience is one of the at least one consumer segment.
 7. Themethod of claim 5 wherein the correlation visualization utilizes thecalculated polarity, emotion and topicality.
 8. The method of claim 1wherein the correlation is generated using clustering algorithms.
 9. Themethod of claim 1 wherein the selected key attributes are name valuepair requests.
 10. The method of claim 1 wherein the generatingsentiment and generating correlations dynamically utilizes modelsaccording to attributes of the integrated data.
 11. A system forgenerating consumer sentiment and attribute correlation visualizationsfrom data obtained from a plurality of data sources, comprising: atleast two integration elements configured to integrate data from aplurality of data sources; at least two attribute selectors configuredto select key attributes from the integrated data; a social segmentelement configured to generate at least one consumer segment using theintegrated data; a sentiments mapping element configured to generatesentiment for the at least one consumer segment using the integrateddata; a correlation mapping element configured to generate correlationsbetween attributes of the integrated data; and a visualization elementconfigured to generate a visualization of the segment, sentiment andcorrelations.
 12. The system of claim 11 wherein the at least twointegration elements employ cloud-based computing technique.
 13. Thesystem of claim 11 wherein the plurality of data sources includes astructured data source, an unstructured data source, a social datasource, and a syndicated data source.
 14. The system of claim 13 whereinthe social segment element generates the at least one consumer segmentemploys the social data source.
 15. The system of claim 11 wherein thesentiments mapping element is further configured to calculate polarity,emotion and topicality for a target and an audience.
 16. The system ofclaim 15 wherein the audience is one of the at least one consumersegment.
 17. The system of claim 15 wherein the visualization elementutilizes the calculated polarity, emotion and topicality to generate thevisualization.
 18. The system of claim 11 wherein the correlationmapping element generates the correlation using clustering algorithms.19. The system of claim 11 wherein the selected key attributes are namevalue pair requests.
 20. The system of claim 11 wherein the sentimentmapping element and the correlation mapping element dynamically utilizemodels according to attributes of the integrated data.